Scaling Political Texts with ChatGPT

We use GPT-4 to obtain position estimates of political texts in continuous spaces. We develop and validate a new approach by positioning British party manifestos on the economic, social, and immigration policy dimensions and tweets by members of the US Congress on the left-right ideological spectrum. For the party manifestos, the correlation between the positions produced by GPT-4 and experts is 93% or higher, a performance similar to or better than that obtained with crowdsourced position estimates. For individual tweets, the positions obtained with GPT-4 achieve a correlation of 91% with crowdsourced position estimates. For senators of the 117th US Congress, the positions obtained with GPT-4 achieve a correlation of 97% with estimates based on roll call votes and of 96% with those based on campaign funding. Correlations are also substantial within party, indicating that position estimates produced with GPT-4 capture within-party differences between senators. Overall, using GPT-4 for ideological scaling is fast, cost-efficient, and reliable. This approach provides a viable alternative to scaling by both expert raters and crowdsourcing

When more selection is worse

We demonstrate a paradox of selection: the average level of skill among the survivors of selection may initially increase but eventually decrease. This result occurs in a simple model in which performance is not frequency dependent, there are no delayed effects, and skill is unrelated to risk-taking. The performance of an agent in any given period equals a skill component plus a noise term. We show that the average skill of survivors eventually decreases when the noise terms in consecutive periods are dependent and drawn from a distribution with a “long” tail—a sub-class of heavy-tailed distributions. This result occurs because only agents with extremely high level of performance survive many periods, and extreme performance is not diagnostic of high skill when the noise term is drawn from a long-tailed distribution

Opinion homogenization and polarization

We describe three sampling models that aim to cast light on how some design features of social media platforms systematically affect judgments of their users. We specify the micro-mechanisms of belief formation and interactions and explore their macro implications such as opinion polarization. Each model focuses on a specific aspect of platform-mediated social interactions: how popularity creates additional exposure to contrarian arguments; how differences in popularity make an agent more likely to hear particularly persuasive arguments in support of popular options; and how opinions in favor of popular options are reinforced through social feedback. We show that these mechanisms lead to self-reinforcing dynamics that can result in local opinion homogenization and between-group polarization. Unlike nonsampling-based approaches, our focus does not lie in peculiarities of information processing such as motivated cognition but instead emphasizes how structural features of the learning environment contribute to opinion homogenization and polarization

Social media feedback and extreme opinion expression

On popular social media platforms such as Twitter, Facebook, Instagram, or Tiktok, the quantitative feedback received by content producers is asymmetric: counts of positive reactions such as ‘likes,’ or ‘retweets,’ are easily observed but similar counts of negative reactions are not directly available. We study how this design feature of social media platforms affects the expression of extreme opinions. Using simulations of a learning model, we compare two feedback environments that differ in terms of the availability of negative reaction counts. We find that expressed opinions are generally more extreme when negative reaction counts are not available than when they are. We rely on analyses of Twitter data and several online experiments to provide empirical support for key model assumptions and test model predictions. Our findings suggest that a simple design change might limit, under certain conditions, the expression of extreme opinions on social media

Revisiting the competency trap

We revisit the competency trap and reexamine when it occurs. We show that a bias against alternatives that improve with practice does not require that learning is myopic in the sense of lacking foresight or failing to explore. The same bias occurs even if learners engage in substantial exploration and have foresight. In fact, we demonstrate that even a rational and foresighted learner, who follow an optimal strategy for balancing exploration and exploitation, will learn to prefer alternatives with initially high payoffs that decrease with practice over alternatives, with identical expected values, that have initially low payoffs that increase with practice. Our results show that a bias against alternatives that improve with practice is due to an asymmetry in error correction rather than to myopic learning. The implication is that a wide range of selection systems, even optimally designed ones, will be biased against late-bloomers.G. Le Mens benefited from financial support from grants \#AEI/FEDER UE-PSI2016-75353 and Ramon y Cajal Fellowship (RYC-2014-15035), from the Spanish MINECO, and ERC Consolidator \#772268 from the European Commission

An information sampling explanation for the in-group heterogeneity effect

People often perceive their in-groups as more heterogeneous than their out-groups. We propose an information sampling explanation for this in-group heterogeneity effect. We note that people frequently obtain larger samples of information about in-groups than about out-groups. Using computer simulations, we show that this asymmetry in sample sizes implies the in-group heterogeneity effect under a wide range of assumptions about how experience affects perceived variability. This is the case even when perceived variability is the outcome of rational information processing, implying that the structure of the environment is sufficient to explain the emergence of the in-group heterogeneity effect. A key assumption of our explanation is that perceived group variability depends on the size of the sample observed about this group. We provide evidence in support for this assumption in two experiments. Our results considerably expand the scope and relevance of a prior sampling explanation proposed by Linville, Fischer, and Salovey (1989). They also complement other explanations that proposed that information about in-groups and out-groups is processed differently.Le Mens benefited from financial support from Southern Denmark University and Grants PSI2013-41909-P, #AEI/FEDER UE-PSI2016-75353, Ramon y Cajal Fellowship (RYC-2014-15035) from the Spanish MINECO, Grant IN[15]_EFG_ECO_2281 from the BBVA Foundation and ERC Consolidator #772268 from the European Commission. E. Konovalova was funded by Spanish MINECO Grant PSI2013-41909-P to G. Le Mens

The hot stove effect

People revisit the restaurants they like and avoid the restaurants with which they had a poor experience. This tendency to approach alternatives believed to be good is usually adaptive but can lead to a systematic bias. Errors of underestimation (an alternative is believed to be worse than it is) will be less likely to be corrected than errors of overestimation (an alternative is believed to be better than it is). Denrell & March (2001) called this asymmetry in error correction the “Hot Stove Effect.” This chapter explains the basic logic behind the Hot Stove Effect and how this bias can explain a range of judgment biases. We review empirical studies that illustrate how risk aversion and mistrust can be explained by the Hot Stove Effect. We also explain why even a rational algorithm can be subject to the same bias

Seeking positive experiences can produce illusory correlations

ndividuals tend to select again alternatives about which they have positive impressions and to avoid alternatives about which they have negative impressions. Here we show how this sequential sampling feature of the information acquisition process leads to the emergence of an illusory correlation between estimates of the attributes of multi-attribute alternatives. The sign of the illusory correlation depends on how the decision maker combines estimates in making her sampling decisions. A positive illusory correlation emerges when evaluations are compensatory or disjunctive and a negative illusory correlation can emerge when evaluations are conjunctive. Our theory provides an alternative explanation for illusory correlations that does not rely on biased information processing nor selective attention to different pieces of information. It provides a new perspective on several well-established empirical phenomena such as the ‘Halo’ effect in personality perception, the relation between proximity and attitudes, and the in-group out-group bias in stereotype formation